class_docstr: Runs an op when a metric has plateaued.
code: def _after_run(self, run_context, run_values, global_step, metrics):
      del run_context
      del run_values
      del global_step
  
      if not self.keep_alive:
          return
  
      if not metrics:
          return
  
      if not list(metrics.values())[0]:
          return
  
      # There should be only a single subdir and a single tag
      steps, vals = list(metrics.values())[0][self._tags[0]]
  
      if not steps:
          return
  
      last_step = steps[-1]
      if last_step == self._last_metric_step_seen:
          return
      self._last_metric_step_seen = last_step
  
      if has_metric_plateaued(
          steps,
          vals,
          num_steps=self._num_plateau_steps,
          delta=self._plateau_delta,
          decrease=self._plateau_decrease,
      ):
          self._should_run_op = True
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import os
  import tensorflow.compat.v1
  from tensorboard.backend.event_processing import event_accumulator
  from tensorboard.backend.event_processing import event_multiplexer
label_desc: Use image node_id 4 for calling the PlateauOpHook obj's underlying member method code with example usage: obj._after_run(run_context, run_values, global_step, metrics) without return types